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Data Mining for Biologists

2009

Book Chapter

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In this tutorial chapter, we review basics about frequent pattern mining algorithms, including itemset mining, association rule mining and graph mining. These algorithms can find frequently appearing substructures in discrete data. They can discover structural motifs, for example, from mutation data, protein structures and chemical compounds. As they have been primarily used for business data, biological applications are not so common yet, but their potential impact would be large. Recent advances in computers including multicore machines and ever increasing memory capacity support the application of such methods to larger datasets. We explain technical aspects of the algorithms, but do not go into details. Current biological applications are summarized and possible future directions are given.

Author(s): Tsuda, K.
Book Title: Biological Data Mining in Protein Interaction Networks
Pages: 14-27
Year: 2009
Month: May
Day: 0
Editors: Li, X. and Ng, S.-K.
Publisher: Medical Information Science Reference

Department(s): Empirical Inference
Bibtex Type: Book Chapter (inbook)

Address: Hershey, PA, USA
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web

BibTex

@inbook{5368,
  title = {Data Mining for Biologists},
  author = {Tsuda, K.},
  booktitle = {Biological Data Mining in Protein Interaction Networks},
  pages = {14-27},
  editors = {Li, X. and Ng, S.-K.},
  publisher = {Medical Information Science Reference},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Hershey, PA, USA},
  month = may,
  year = {2009},
  month_numeric = {5}
}